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CPPred-RF
Integrates multiple sequence-based feature descriptors to sufficiently explore distinct information embedded in cell-penetrating peptides (CPPs). CPPred-RF is a machine-learning-based predicator that employs a well-established feature selection technique to improve the feature representation and construct a two-layer prediction framework based on the random forest algorithm. CPPred-RF is competitive or better than the state-of-the-art predictors in terms of predicting CPPs and their uptake efficiency.
SkipCPP-Pred
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Predicts potential novel cell-penetrating peptides (CPPs). SkipCPP-Pred implements an adaptive k-skip feature representation algorithm that captures the correlation information of residues and build the prediction model based on the random forest (RF) classifier. The software indicates if the queried sequences provided by the user is cell-penetrating peptide or not, as well as the prediction confidence. SkipCPP-Pred was assessed by evaluation metrics and a validation method.
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